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Record W2964431599 · doi:10.1016/j.jhepr.2019.07.004

Recent advances in liver transplantation for cancer: The future of transplant oncology

2019· review· en· W2964431599 on OpenAlex
Phillipe Abreu, Andre Gorgen, Graziano Oldani, Taizo Hibi, Gonzalo Sapisochín

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJHEP Reports · 2019
Typereview
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsUniversity Health NetworkToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineLiver transplantationHepatocellular carcinomaTransplantationColorectal cancerNeuroendocrine tumorsInternal medicineCancerOncology

Abstract

fetched live from OpenAlex

Liver transplantation is widely indicated as a curative treatment for selected patients with hepatocellular carcinoma. However, with recent therapeutic advances, as well as efforts to increase the donor pool, liver transplantation has been carefully expanded to patients with other primary or secondary malignancies in the liver. Cholangiocarcinoma, colorectal and neuroendocrine liver metastases, and hepatic epithelioid haemangioendothelioma are amongst the most relevant new indications. In this review we discuss the fundamental concepts of this ambitious undertaking, as well as the newest indications for liver transplantation, with a special focus on future perspectives within the recently established concept of transplant oncology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.370
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it